Bootstrap unsupervised learning

ABSTRACT

Systems, and method and computer readable media that store instructions for motion based object detection. The method may include receiving or generating a video stream that comprises a sequence of images; generating image signatures of the images; wherein each image is associated with an image signature that comprises identifiers; wherein each identifier identifiers a region of interest within the image; generating movement information indicative of movements of the regions of interest within consecutive images of the sequence of images; searching, based on the movement information, for a first group of regions of interest that follow a first movement; wherein different first regions of interest are associated with different parts of an object; and linking between first identifiers that identify the first group of regions of interest.

CROSS REFERENCE

This application claims priority from U.S. provisional patent 62/827,112filing date Mar. 31, 2019 which is incorporated herein by reference.

BACKGROUND

Object detection has extensive usage in variety of applications,starting from security, sport events, automatic vehicles, and the like.

Vast amounts of media units are processed during object detection andtheir processing may require vast amounts of computational resources andmemory resources.

Furthermore—many object detection process are sensitive to variousacquisition parameters such as angle of acquisition, scale, and thelike.

There is a growing need to provide robust and efficient object detectionmethods.

SUMMARY

There may be provided systems, methods and computer readable medium asillustrated in the specification.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the disclosure will be understood and appreciatedmore fully from the following detailed description, taken in conjunctionwith the drawings in which:

FIG. 1A illustrates an example of a method;

FIG. 1B illustrates an example of a signature;

FIG. 1C illustrates an example of a dimension expansion process;

FIG. 1D illustrates an example of a merge operation;

FIG. 1E illustrates an example of hybrid process;

FIG. 1F illustrates an example of a first iteration of the dimensionexpansion process;

FIG. 1G illustrates an example of a method;

FIG. 1H illustrates an example of a method;

FIG. 1I illustrates an example of a method;

FIG. 1J illustrates an example of a method;

FIG. 1K illustrates an example of a method;

FIG. 1L illustrates an example of a method;

FIG. 1M illustrates an example of a method;

FIG. 1N illustrates an example of a matching process and a generation ofa higher accuracy shape information;

FIG. 1O illustrates an example of an image and image identifiers;

FIG. 1P illustrates an example of an image, approximated regions ofinterest, compressed shape information and image identifiers;

FIG. 1Q illustrates an example of an image, approximated regions ofinterest, compressed shape information and image identifiers;

FIG. 1R illustrates an example of an image;

FIG. 1S illustrates an example of a method;

FIG. 2A illustrates an example of images of different scales;

FIG. 2B illustrates an example of images of different scales;

FIG. 2C illustrates an example of a method;

FIG. 2D illustrates an example of a method;

FIG. 2E illustrates an example of a method;

FIG. 2F illustrates an example of a method;

FIG. 2G illustrates an example of different images;

FIG. 2H illustrates an example of a method;

FIG. 2I illustrates an example of a method;

FIG. 2J illustrates an example of a method;

FIG. 2K illustrates an example of different images acquisition angles;

FIG. 2L illustrates an example of a method;

FIG. 2M illustrates an example of a method; and

FIG. 2N illustrates an example of a system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

Because the illustrated embodiments of the present invention may for themost part, be implemented using electronic components and circuits knownto those skilled in the art, details will not be explained in anygreater extent than that considered necessary as illustrated above, forthe understanding and appreciation of the underlying concepts of thepresent invention and in order not to obfuscate or distract from theteachings of the present invention.

Any reference in the specification to a method should be applied mutatismutandis to a device or system capable of executing the method and/or toa non-transitory computer readable medium that stores instructions forexecuting the method.

Any reference in the specification to a system or device should beapplied mutatis mutandis to a method that may be executed by the system,and/or may be applied mutatis mutandis to non-transitory computerreadable medium that stores instructions executable by the system.

Any reference in the specification to a non-transitory computer readablemedium should be applied mutatis mutandis to a device or system capableof executing instructions stored in the non-transitory computer readablemedium and/or may be applied mutatis mutandis to a method for executingthe instructions.

Any combination of any module or unit listed in any of the figures, anypart of the specification and/or any claims may be provided.

The specification and/or drawings may refer to an image. An image is anexample of a media unit. Any reference to an image may be appliedmutatis mutandis to a media unit. A media unit may be an example ofsensed information. Any reference to a media unit may be applied mutatismutandis to a natural signal such as but not limited to signal generatedby nature, signal representing human behavior, signal representingoperations related to the stock market, a medical signal, and the like.Any reference to a media unit may be applied mutatis mutandis to sensedinformation. The sensed information may be sensed by any type ofsensors—such as a visual light camera, or a sensor that may senseinfrared, radar imagery, ultrasound, electro-optics, radiography, LIDAR(light detection and ranging), etc.

The specification and/or drawings may refer to a processor. Theprocessor may be a processing circuitry. The processing circuitry may beimplemented as a central processing unit (CPU), and/or one or more otherintegrated circuits such as application-specific integrated circuits(ASICs), field programmable gate arrays (FPGAs), full-custom integratedcircuits, etc., or a combination of such integrated circuits.

Any combination of any steps of any method illustrated in thespecification and/or drawings may be provided.

Any combination of any subject matter of any of claims may be provided.

Any combinations of systems, units, components, processors, sensors,illustrated in the specification and/or drawings may be provided.

Low Power Generation of Signatures

The analysis of content of a media unit may be executed by generating asignature of the media unit and by comparing the signature to referencesignatures. The reference signatures may be arranged in one or moreconcept structures or may be arranged in any other manner. Thesignatures may be used for object detection or for any other use.

The signature may be generated by creating a multidimensionalrepresentation of the media unit. The multidimensional representation ofthe media unit may have a very large number of dimensions. The highnumber of dimensions may guarantee that the multidimensionalrepresentation of different media units that include different objectsis sparse—and that object identifiers of different objects are distantfrom each other—thus improving the robustness of the signatures.

The generation of the signature is executed in an iterative manner thatincludes multiple iterations, each iteration may include an expansionoperations that is followed by a merge operation. The expansionoperation of an iteration is performed by spanning elements of thatiteration. By determining, per iteration, which spanning elements (ofthat iteration) are relevant—and reducing the power consumption ofirrelevant spanning elements—a significant amount of power may be saved.

In many cases, most of the spanning elements of an iteration areirrelevant—thus after determining (by the spanning elements) theirrelevancy—the spanning elements that are deemed to be irrelevant may beshut down a/or enter an idle mode.

FIG. 1A illustrates a method 5000 for generating a signature of a mediaunit.

Method 5000 may start by step 5010 of receiving or generating sensedinformation.

The sensed information may be a media unit of multiple objects.

Step 5010 may be followed by processing the media unit by performingmultiple iterations, wherein at least some of the multiple iterationscomprises applying, by spanning elements of the iteration, dimensionexpansion process that are followed by a merge operation.

The processing may include:

-   -   Step 5020 of performing a k'th iteration expansion process (k        may be a variable that is used to track the number of        iterations).    -   Step 5030 of performing a k'th iteration merge process.    -   Step 5040 of changing the value of k.    -   Step 5050 of checking if all required iterations were done—if so        proceeding to step 5060 of completing the generation of the        signature. Else—jumping to step 5020.

The output of step 5020 is a k'th iteration expansion results 5120.

The output of step 5030 is a k'th iteration merge results 5130.

For each iteration (except the first iteration)—the merge result of theprevious iteration is an input to the current iteration expansionprocess.

At least some of the K iterations involve selectively reducing the powerconsumption of some spanning elements (during step 5020) that are deemedto be irrelevant.

FIG. 1B is an example of an image signature 6027 of a media unit that isan image 6000 and of an outcome 6013 of the last (K'th) iteration.

The image 6001 is virtually segments to segments 6000(i,k). The segmentsmay be of the same shape and size but this is not necessarily so.

Outcome 6013 may be a tensor that includes a vector of values per eachsegment of the media unit. One or more objects may appear in a certainsegment. For each object—an object identifier (of the signature) pointsto locations of significant values, within a certain vector associatedwith the certain segment.

For example—a top left segment (6001(1,1)) of the image may berepresented in the outcome 6013 by a vector V(1,1) 6017(1,1) that hasmultiple values. The number of values per vector may exceed 100, 200,500, 1000, and the like.

The significant values (for example—more than 10, 20, 30, 40 values,and/or more than 0.1%, 0.2%. 0.5%, 1%, 5% of all values of the vectorand the like) may be selected. The significant values may have thevalues—but maybe selected in any other manner.

FIG. 1B illustrates a set of significant responses 6015(1,1) of vectorV(1,1) 6017(1,1). The set includes five significant values (such asfirst significant value SV1(1,1) 6013(1,1,1), second significant valueSV2(1,1), third significant value SV3(1,1), fourth significant valueSV4(1,1), and fifth significant value SV5(1,1) 6013(1,1,5).

The image signature 6027 includes five indexes for the retrieval of thefive significant values—first till fifth identifiers ID1—ID5 are indexesfor retrieving the first till fifth significant values.

FIG. 1C illustrates a k'th iteration expansion process.

The k'th iteration expansion process start by receiving the mergeresults 5060′ of a previous iteration.

The merge results of a previous iteration may include values areindicative of previous expansion processes—for example—may includevalues that are indicative of relevant spanning elements from a previousexpansion operation, values indicative of relevant regions of interestin a multidimensional representation of the merge results of a previousiteration.

The merge results (of the previous iteration) are fed to spanningelements such as spanning elements 5061(1)-5061(J).

Each spanning element is associated with a unique set of values. The setmay include one or more values. The spanning elements apply differentfunctions that may be orthogonal to each other. Using non-orthogonalfunctions may increase the number of spanning elements—but thisincrement may be tolerable.

The spanning elements may apply functions that are decorrelated to eachother—even if not orthogonal to each other.

The spanning elements may be associated with different combinations ofobject identifiers that may “cover” multiple possible media units.Candidates for combinations of object identifiers may be selected invarious manners—for example based on their occurrence in various images(such as test images) randomly, pseudo randomly, according to some rulesand the like. Out of these candidates the combinations may be selectedto be decorrelated, to cover said multiple possible media units and/orin a manner that certain objects are mapped to the same spanningelements.

Each spanning element compares the values of the merge results to theunique set (associated with the spanning element) and if there is amatch—then the spanning element is deemed to be relevant. If so—thespanning element completes the expansion operation.

If there is no match—the spanning element is deemed to be irrelevant andenters a low power mode. The low power mode may also be referred to asan idle mode, a standby mode, and the like. The low power mode is termedlow power because the power consumption of an irrelevant spanningelement is lower than the power consumption of a relevant spanningelement.

In FIG. 1C various spanning elements are relevant (5061(1)-5061(3)) andone spanning element is irrelevant (5061(J)).

Each relevant spanning element may perform a spanning operation thatincludes assigning an output value that is indicative of an identity ofthe relevant spanning elements of the iteration. The output value mayalso be indicative of identities of previous relevant spanning elements(from previous iterations).

For example—assuming that spanning element number fifty is relevant andis associated with a unique set of values of eight and four—then theoutput value may reflect the numbers fifty, four and eight—for exampleone thousand multiplied by (fifty+forty) plus forty. Any other mappingfunction may be applied.

FIG. 1C also illustrates the steps executed by each spanning element:

-   -   Checking if the merge results are relevant to the spanning        element (step 5091).    -   If—so—completing the spanning operation (step 5093).    -   If not—entering an idle state (step 5092).

FIG. 1D is an example of various merge operations.

A merge operation may include finding regions of interest. The regionsof interest are regions within a multidimensional representation of thesensed information. A region of interest may exhibit a more significantresponse (for example a stronger, higher intensity response).

The merge operation (executed during a k'th iteration merge operation)may include at least one of the following:

-   -   Step 5031 of searching for overlaps between regions of interest        (of the k'th iteration expansion operation results) and define        regions of interest that are related to the overlaps.    -   Step 5032 of determining to drop one or more region of interest,        and dropping according to the determination.    -   Step 5033 of searching for relationships between regions of        interest (of the k'th iteration expansion operation results) and        define regions of interest that are related to the relationship.    -   Step 5034 of searching for proximate regions of interest (of the        k'th iteration expansion operation results) and define regions        of interest that are related to the proximity. Proximate may be        a distance that is a certain fraction (for example less than 1%)        of the multi-dimensional space, may be a certain fraction of at        least one of the regions of interest that are tested for        proximity.    -   Step 5035 of searching for relationships between regions of        interest (of the k'th iteration expansion operation results) and        define regions of interest that are related to the relationship.    -   Step 5036 of merging and/or dropping k'th iteration regions of        interest based on shape information related to shape of the k'th        iteration regions of interest.

The same merge operations may applied in different iterations.

Alternatively, different merge operations may be executed duringdifferent iterations.

FIG. 1E illustrates an example of a hybrid process and an input image6001.

The hybrid process is hybrid in the sense that some expansion and mergeoperations are executed by a convolutional neural network (CNN) and someexpansion and merge operations (denoted additional iterations ofexpansion and merge) are not executed by the CNN— but rather by aprocess that may include determining a relevancy of spanning elementsand entering irrelevant spanning elements to a low power mode.

In FIG. 1E one or more initial iterations are executed by first andsecond CNN layers 6010(1) and 6010(2) that apply first and secondfunctions 6015(1) and 6015(2).

The output of these layers provided information about image properties.The image properties may not amount to object detection. Imageproperties may include location of edges, properties of curves, and thelike.

The CNN may include additional layers (for example third till N'th layer6010(N)) that may provide a CNN output 6018 that may include objectdetection information. It should be noted that the additional layers maynot be included.

It should be noted that executing the entire signature generationprocess by a hardware CNN of fixed connectivity may have a higher powerconsumption—as the CNN will not be able to reduce the power consumptionof irrelevant nodes.

FIG. 1F illustrates an input image 6001, and a single iteration of anexpansion operation and a merge operation.

In FIG. 1F the input image 6001 undergoes two expansion operations.

The first expansion operation involves filtering the input image by afirst filtering operation 6031 to provide first regions of interest(denoted 1) in a first filtered image 6031′.

The first expansion operation also involves filtering the input image bya second filtering operation 6032 to provide first regions of interest(denoted 2) in a second filtered image 6032′,

The merge operation includes merging the two images by overlaying thefirst filtered image on the second filtered image to provide regions ofinterest 1, 2, 12 and 21. Region of interest 12 is an overlap areashared by a certain region of interest 1 and a certain region ofinterest 2. Region of interest 21 is a union of another region ofinterest 1 and another region of interest 2.

FIG. 1G illustrates method 5200 for generating a signature.

Method 5200 may include the following sequence of steps:

-   -   Step 5210 of receiving or generating an image.    -   Step 5220 of performing a first iteration expansion operation        (which is an expansion operation that is executed during a first        iteration)    -   Step 5230 of performing a first iteration merge operation.    -   Step 5240 of amending index k (k is an iteration counter). In        FIG. 7 in incremented by one—this is only an example of how the        number of iterations are tracked.    -   Step 5260 of performing a k'th iteration expansion operation on        the (k−1)'th iteration merge results.    -   Step 5270 of performing a k'th iteration merge operation (on the        k'th iteration expansion operation results.    -   Step 5280 of changing the value of index k.    -   Step 5290 of checking if all iteration ended (k reached its        final value—for example K).

If no—there are still iterations to be executed—jumping from step 5290to step 5260.

If yes—jumping to step 5060 of completing the generation of thesignature. This may include, for example, selecting significantattributes, determining retrieval information (for example indexes) thatpoint to the selected significant attributes.

Step 5220 may include:

-   -   Step 5222 of generating multiple representations of the image        within a multi-dimensional space of f(1) dimensions. The        expansion operation of step 5220 generates a first iteration        multidimensional representation of the first image. The number        of dimensions of this first iteration multidimensional        representation is denoted f(1).    -   Step 5224 of assigning a unique index for each region of        interest within the multiple representations. For example,        referring to FIG. 6—indexes 1 and indexes 2 are assigned to        regions of interests generated during the first iteration        expansion operations 6031 and 6032.

Step 5230 may include:

-   -   Step 5232 of searching for relationships between regions of        interest and define regions of interest that are related to the        relationships. For example—union or intersection illustrate din        FIG. 6.    -   Step 5234 of assigning a unique index for each region of        interest within the multiple representations. For        example—referring to FIG. 6—indexes 1,2, 12 and 21.

Step 5260 may include:

-   -   Step 5262 of generating multiple representations of the merge        results of the (k−1)'th iteration within a multi-dimensional        space of f(k) dimensions. The expansion operation of step 5260        generates a k'th iteration multidimensional representation of        the first image. The number of dimensions of this kth iteration        multidimensional representation is denoted f(k).    -   Step 5264 of assigning a unique index for each region of        interest within the multiple representations.

Step 5270 may include

-   -   Step 5272 of searching for relationships between regions of        interest and define regions of interest that are related to the        relationships.    -   Step 5274 of Assigning a unique index for each region of        interest within the multiple representations.

FIG. 1H illustrates a method 5201. In method 5201 the relationshipsbetween the regions of interest are overlaps.

Thus—step 5232 is replaced by step 5232′ of searching for overlapsbetween regions of interest and define regions of interest that arerelated to the overlaps.

Step 5272 is replaced by step 5272′ of searching for overlaps betweenregions of interest and define regions of interest that are related tothe overlaps.

FIG. 1I illustrates a method 7000 for low-power calculation of asignature.

Method 7000 starts by step 7010 of receiving or generating a media unitof multiple objects.

Step 7010 may be followed by step 7012 of processing the media unit byperforming multiple iterations, wherein at least some of the multipleiterations comprises applying, by spanning elements of the iteration,dimension expansion process that are followed by a merge operation.

The applying of the dimension expansion process of an iteration mayinclude (a) determining a relevancy of the spanning elements of theiteration; and (b) completing the dimension expansion process byrelevant spanning elements of the iteration and reducing a powerconsumption of irrelevant spanning elements until, at least, acompletion of the applying of the dimension expansion process.

The identifiers may be retrieval information for retrieving thesignificant portions.

The at least some of the multiple iterations may be a majority of themultiple iterations.

The output of the multiple iteration may include multiple propertyattributes for each segment out of multiple segments of the media unit;and wherein the significant portions of an output of the multipleiterations may include more impactful property attributes.

The first iteration of the multiple iteration may include applying thedimension expansion process by applying different filters on the mediaunit.

The at least some of the multiple iteration exclude at least a firstiteration of the multiple iterations. See, for example, FIG. 1E.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iterationmay be performed by the spanning elements of the iteration.

Method 7000 may include a neural network processing operation that maybe executed by one or more layers of a neural network and does notbelong to the at least some of the multiple iterations. See, forexample, FIG. 1E.

The at least one iteration may be executed without reducing powerconsumption of irrelevant neurons of the one or more layers.

The one or more layers may output information about properties of themedia unit, wherein the information differs from a recognition of themultiple objects.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative of an identity of the relevantspanning elements of the iteration. See, for example, FIG. 1C.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative a history of dimension expansionprocesses until the iteration that differs from the first iteration.

The each spanning element may be associated with a subset of referenceidentifiers. The determining of the relevancy of each spanning elementsof the iteration may be based a relationship between the subset of thereference identifiers of the spanning element and an output of a lastmerge operation before the iteration.

The output of a dimension expansion process of an iteration may be amultidimensional representation of the media unit that may include mediaunit regions of interest that may be associated with one or moreexpansion processes that generated the regions of interest.

The merge operation of the iteration may include selecting a subgroup ofmedia unit regions of interest based on a spatial relationship betweenthe subgroup of multidimensional regions of interest. See, for example,FIGS. 3 and 6.

Method 7000 may include applying a merge function on the subgroup ofmultidimensional regions of interest. See, for example, FIGS. 1C and 1F.

Method 7000 may include applying an intersection function on thesubgroup of multidimensional regions of interest. See, for example,FIGS. 1C and 1F.

The merge operation of the iteration may be based on an actual size ofone or more multidimensional regions of interest.

The merge operation of the iteration may be based on relationshipbetween sizes of the multidimensional regions of interest. Forexample—larger multidimensional regions of interest may be maintainedwhile smaller multidimensional regions of interest may be ignored of.

The merge operation of the iteration may be based on changes of themedia unit regions of interest during at least the iteration and one ormore previous iteration.

Step 7012 may be followed by step 7014 of determining identifiers thatare associated with significant portions of an output of the multipleiterations.

Step 7014 may be followed by step 7016 of providing a signature thatcomprises the identifiers and represents the multiple objects.

Localization and Segmentation

Any of the mentioned above signature generation method provides asignature that does not explicitly includes accurate shape information.This adds to the robustness of the signature to shape relatedinaccuracies or to other shape related parameters.

The signature includes identifiers for identifying media regions ofinterest.

Each media region of interest may represent an object (for example avehicle, a pedestrian, a road element, a human made structure,wearables, shoes, a natural element such as a tree, the sky, the sun,and the like) or a part of an object (for example—in the case of thepedestrian—a neck, a head, an arm, a leg, a thigh, a hip, a foot, anupper arm, a forearm, a wrist, and a hand). It should be noted that forobject detection purposes a part of an object may be regarded as anobject.

The exact shape of the object may be of interest.

FIG. 1J illustrates method 7002 of generating a hybrid representation ofa media unit.

Method 7002 may include a sequence of steps 7020, 7022, 7024 and 7026.

Step 7020 may include receiving or generating the media unit.

Step 7022 may include processing the media unit by performing multipleiterations, wherein at least some of the multiple iterations comprisesapplying, by spanning elements of the iteration, dimension expansionprocess that are followed by a merge operation.

Step 7024 may include selecting, based on an output of the multipleiterations, media unit regions of interest that contributed to theoutput of the multiple iterations.

Step 7026 may include providing a hybrid representation, wherein thehybrid representation may include (a) shape information regarding shapesof the media unit regions of interest, and (b) a media unit signaturethat includes identifiers that identify the media unit regions ofinterest.

Step 7024 may include selecting the media regions of interest persegment out of multiple segments of the media unit. See, for example,FIG. 2.

Step 7026 may include step 7027 of generating the shape information.

The shape information may include polygons that represent shapes thatsubstantially bound the media unit regions of interest. These polygonsmay be of a high degree.

In order to save storage space, the method may include step 7028 ofcompressing the shape information of the media unit to providecompressed shape information of the media unit.

FIG. 1K illustrates method 5002 for generating a hybrid representationof a media unit.

Method 5002 may start by step 5011 of receiving or generating a mediaunit.

Step 5011 may be followed by processing the media unit by performingmultiple iterations, wherein at least some of the multiple iterationscomprises applying, by spanning elements of the iteration, dimensionexpansion process that are followed by a merge operation.

The processing may be followed by steps 5060 and 5062.

The processing may include steps 5020, 5030, 5040 and 5050.

Step 5020 may include performing a k'th iteration expansion process (kmay be a variable that is used to track the number of iterations).

Step 5030 may include performing a k'th iteration merge process.

Step 5040 may include changing the value of k.

Step 5050 may include checking if all required iterations were done—ifso proceeding to steps 5060 and 5062. Else—jumping to step 5020.

The output of step 5020 is a k'th iteration expansion result.

The output of step 5030 is a k'th iteration merge result.

For each iteration (except the first iteration)—the merge result of theprevious iteration is an input to the current iteration expansionprocess.

Step 5060 may include completing the generation of the signature.

Step 5062 may include generating shape information regarding shapes ofmedia unit regions of interest. The signature and the shape informationprovide a hybrid representation of the media unit.

The combination of steps 5060 and 5062 amounts to a providing a hybridrepresentation, wherein the hybrid representation may include (a) shapeinformation regarding shapes of the media unit regions of interest, and(b) a media unit signature that includes identifiers that identify themedia unit regions of interest.

FIG. 1L illustrates method 5203 for generating a hybrid representationof an image.

Method 5200 may include the following sequence of steps:

-   -   Step 5210 of receiving or generating an image.    -   Step 5230 of performing a first iteration expansion operation        (which is an expansion operation that is executed during a first        iteration)    -   Step 5240 of performing a first iteration merge operation.    -   Step 5240 of amending index k (k is an iteration counter). In        FIG. 1L in incremented by one—this is only an example of how the        number of iterations are tracked.    -   Step 5260 of performing a k'th iteration expansion operation on        the (k−1)'th iteration merge results.    -   Step 5270 of Performing a k'th iteration merge operation (on the        k'th iteration expansion operation results.    -   Step 5280 of changing the value of index k.    -   Step 5290 of checking if all iteration ended (k reached its        final value—for example K).

If no—there are still iterations to be executed—jumping from step 5290to step 5260.

If yes—jumping to step 5060.

Step 5060 may include completing the generation of the signature. Thismay include, for example, selecting significant attributes, determiningretrieval information (for example indexes) that point to the selectedsignificant attributes.

Step 5062 may include generating shape information regarding shapes ofmedia unit regions of interest. The signature and the shape informationprovide a hybrid representation of the media unit.

The combination of steps 5060 and 5062 amounts to a providing a hybridrepresentation, wherein the hybrid representation may include (a) shapeinformation regarding shapes of the media unit regions of interest, and(b) a media unit signature that includes identifiers that identify themedia unit regions of interest.

Step 5220 may include:

-   -   Step 5222 of generating multiple representations of the image        within a multi-dimensional space of f(k) dimensions.    -   Step 5224 of assigning a unique index for each region of        interest within the multiple representations. (for example,        referring to FIG. 1F—indexes 1 and indexes 2 following first        iteration expansion operations 6031 and 6032.

Step 5230 may include

-   -   Step 5226 of searching for relationships between regions of        interest and define regions of interest that are related to the        relationships. For example—union or intersection illustrated in        FIG. 1F.    -   Step 5228 of assigning a unique index for each region of        interest within the multiple representations. For        example—referring to FIG. 1F—indexes 1, 2, 12 and 21.

Step 5260 may include:

-   -   Step 5262 of generating multiple representations of the merge        results of the (k−1)'th iteration within a multi-dimensional        space of f(k) dimensions. The expansion operation of step 5260        generates a k'th iteration multidimensional representation of        the first image. The number of dimensions of this kth iteration        multidimensional representation is denoted f(k).    -   Step 5264 of assigning a unique index for each region of        interest within the multiple representations.

Step 5270 may include

-   -   Step 5272 of searching for relationships between regions of        interest and define regions of interest that are related to the        relationships.    -   Step 5274 of assigning a unique index for each region of        interest within the multiple representations.

FIG. 1M illustrates method 5205 for generating a hybrid representationof an image.

Method 5200 may include the following sequence of steps:

-   -   Step 5210 of receiving or generating an image.    -   Step 5230 of performing a first iteration expansion operation        (which is an expansion operation that is executed during a first        iteration)    -   Step 5240 of performing a first iteration merge operation.    -   Step 5240 of amending index k (k is an iteration counter). In        FIG. 1M in incremented by one—this is only an example of how the        number of iterations are tracked.    -   Step 5260 of performing a k'th iteration expansion operation on        the (k−1)'th iteration merge results.    -   Step 5270 of performing a k'th iteration merge operation (on the        k'th iteration expansion operation results.    -   Step 5280 of changing the value of index k.    -   Step 5290 of checking if all iteration ended (k reached its        final value—for example K).

If no—there are still iterations to be executed—jumping from step 5290to step 5260.

If yes—jumping to steps 5060 and 5062.

Step 5060 may include completing the generation of the signature. Thismay include, for example, selecting significant attributes, determiningretrieval information (for example indexes) that point to the selectedsignificant attributes.

Step 5062 may include generating shape information regarding shapes ofmedia unit regions of interest. The signature and the shape informationprovide a hybrid representation of the media unit.

The combination of steps 5060 and 5062 amounts to a providing a hybridrepresentation, wherein the hybrid representation may include (a) shapeinformation regarding shapes of the media unit regions of interest, and(b) a media unit signature that includes identifiers that identify themedia unit regions of interest.

Step 5220 may include:

-   -   Step 5221 of filtering the image with multiple filters that are        orthogonal to each other to provide multiple filtered images        that are representations of the image in a multi-dimensional        space of f(1) dimensions. The expansion operation of step 5220        generates a first iteration multidimensional representation of        the first image. The number of filters is denoted f(1).    -   Step 5224 of assigning a unique index for each region of        interest within the multiple representations. (for example,        referring to FIG. 1F—indexes 1 and indexes 2 following first        iteration expansion operations 6031 and 6032.

Step 5230 may include

-   -   Step 5226 of searching for relationships between regions of        interest and define regions of interest that are related to the        relationships. For example—union or intersection illustrated in        FIG. 1F.    -   Step 5228 of assigning a unique index for each region of        interest within the multiple representations. For        example—referring to FIG. 1F—indexes 1, 2, 12 and 21.

Step 5260 may include:

-   -   Step 5262 of generating multiple representations of the merge        results of the (k−1)'th iteration within a multi-dimensional        space of f(k) dimensions. The expansion operation of step 5260        generates a k'th iteration multidimensional representation of        the first image. The number of dimensions of this kth iteration        multidimensional representation is denoted f(k).    -   Step 5264 of assigning a unique index for each region of        interest within the multiple representations.

Step 5270 may include

-   -   Step 5272 of searching for relationships between regions of        interest and define regions of interest that are related to the        relationships.    -   Step 5274 of assigning a unique index for each region of        interest within the multiple representations.

The filters may be orthogonal may be non-orthogonal—for example bedecorrelated. Using non-orthogonal filters may increase the number offilters—but this increment may be tolerable.

Object Detection Using Compressed Shape Information

Object detection may include comparing a signature of an input image tosignatures of one or more cluster structures in order to find one ormore cluster structures that include one or more matching signaturesthat match the signature of the input image.

The number of input images that are compared to the cluster structuresmay well exceed the number of signatures of the cluster structures. Forexample—thousands, tens of thousands, hundreds of thousands (and evenmore) of input signature may be compared to much less cluster structuresignatures. The ratio between the number of input images to theaggregate number of signatures of all the cluster structures may exceedten, one hundred, one thousand, and the like.

In order to save computational resources, the shape information of theinput images may be compressed.

On the other hand—the shape information of signatures that belong to thecluster structures may be uncompressed—and of higher accuracy than thoseof the compressed shape information.

When the higher quality is not required—the shape information of thecluster signature may also be compressed.

Compression of the shape information of cluster signatures may be basedon a priority of the cluster signature, a popularity of matches to thecluster signatures, and the like.

The shape information related to an input image that matches one or moreof the cluster structures may be calculated based on shape informationrelated to matching signatures.

For example—a shape information regarding a certain identifier withinthe signature of the input image may be determined based on shapeinformation related to the certain identifiers within the matchingsignatures.

Any operation on the shape information related to the certainidentifiers within the matching signatures may be applied in order todetermine the (higher accuracy) shape information of a region ofinterest of the input image identified by the certain identifier.

For example—the shapes may be virtually overlaid on each other and thepopulation per pixel may define the shape.

For example—only pixels that appear in at least a majority of theoverlaid shaped should be regarded as belonging to the region ofinterest.

Other operations may include smoothing the overlaid shapes, selectingpixels that appear in all overlaid shapes.

The compressed shape information may be ignored of or be taken intoaccount.

FIG. 1N illustrates method 7003 of determining shape information of aregion of interest of a media unit.

Method 7003 may include a sequence of steps 7030, 7032 and 7034.

Step 7030 may include receiving or generating a hybrid representation ofa media unit. The hybrid representation includes compressed shapeinformation.

Step 7032 may include comparing the media unit signature of the mediaunit to signatures of multiple concept structures to find a matchingconcept structure that has at least one matching signature that matchesto the media unit signature.

Step 7034 may include calculating higher accuracy shape information thatis related to regions of interest of the media unit, wherein the higheraccuracy shape information is of higher accuracy than the compressedshape information of the media unit, wherein the calculating is based onshape information associated with at least some of the matchingsignatures.

Step 7034 may include at least one out of:

-   -   Determining shapes of the media unit regions of interest using        the higher accuracy shape information.    -   For each media unit region of interest, virtually overlaying        shapes of corresponding media units of interest of at least some        of the matching signatures.

FIG. 1O illustrates a matching process and a generation of a higheraccuracy shape information.

It is assumed that there are multiple (M) cluster structures4974(1)-4974(M). Each cluster structure includes cluster signatures,metadata regarding the cluster signatures, and shape informationregarding the regions of interest identified by identifiers of thecluster signatures.

For example—first cluster structure 4974(1) includes multiple (N1)signatures (referred to as cluster signatures CS) CS(1,1)-CS(1,N1)4975(1,1)-4975(1,N1), metadata 4976(1), and shape information (Shapeinfo4977(1)) regarding shapes of regions of interest associated withidentifiers of the CSs.

Yet for another example—M'th cluster structure 4974(M) includes multiple(N2) signatures (referred to as cluster signatures CS) CS(M,1)-CS(M,N2)4975(M,1)-4975(M,N2), metadata 4976(M), and shape information (Shapeinfo4977(M)) regarding shapes of regions of interest associated withidentifiers of the CSs.

The number of signatures per concept structure may change over time—forexample due to cluster reduction attempts during which a CS is removedfrom the structure to provide a reduced cluster structure, the reducedstructure is checked to determine that the reduced cluster signature maystill identify objects that were associated with the (non-reduced)cluster signature—and if so the signature may be reduced from thecluster signature.

The signatures of each cluster structures are associated to each other,wherein the association may be based on similarity of signatures and/orbased on association between metadata of the signatures.

Assuming that each cluster structure is associated with a uniqueobject—then objects of a media unit may be identified by finding clusterstructures that are associated with said objects. The finding of thematching cluster structures may include comparing a signature of themedia unit to signatures of the cluster structures- and searching forone or more matching signature out of the cluster signatures.

In FIG. 1O—a media unit having a hybrid representation undergoes objectdetection. The hybrid representation includes media unit signature 4972and compressed shape information 4973.

The media unit signature 4972 is compared to the signatures of the Mcluster structures—from CS(1,1) 4975(1,1) till CS(M,N2) 4975(M,N2).

We assume that one or more cluster structures are matching clusterstructures.

Once the matching cluster structures are found the method proceeds bygenerating shape information that is of higher accuracy then thecompressed shape information.

The generation of the shape information is done per identifier.

For each j that ranges between 1 and J (J is the number of identifiersper the media unit signature 4972) the method may perform the steps of:

-   -   Find (step 4978(j)) the shape information of the j'th identifier        of each matching signature- or of each signature of the matching        cluster structure.    -   Generate (step 4979(j)) a higher accuracy shape information of        the j'th identifier.

For example—assuming that the matching signatures include CS(1,1)2975(1,1), CS(2,5) 2975(2,5), CS(7,3) 2975(7,3) and CS(15,2) 2975(15,2),and that the j'th identifier is included in CS(1,1) 2975(1,1), CS(7,3)2975(7,3) and CS(15,2) 2975(15,2)—then the shape information of the j'thidentifier of the media unit is determined based on the shapeinformation associated with CS(1,1) 2975(1,1),CS(7,3) 2975(7,3) andCS(15,2) 2975(15,2).

FIG. 1P illustrates an image 8000 that includes four regions of interest8001, 8002, 8003 and 8004. The signature 8010 of image 8000 includesvarious identifiers including ID1 8011, ID2 8012, ID3 8013 and ID4 8014that identify the four regions of interest 8001, 8002, 8003 and 8004.

The shapes of the four regions of interest 8001, 8002, 8003 and 8004 arefour polygons. Accurate shape information regarding the shapes of theseregions of interest may be generated during the generation of signature8010.

FIG. 1Q illustrates the compressing of the shape information torepresent a compressed shape information that reflects simplerapproximations (8001′, 8002′, 8003′ and 8004′) of the regions ofinterest 8001, 8002, 8003 and 8004. In this example simpler may includeless facets, fewer values of angles, and the like.

The hybrid representation of the media unit, after compression representan media unit with simplified regions of interest 8001′, 8002′, 8003′and 8004′—as shown in FIG. 1R.

Scale Based Bootstrap

Objects may appear in an image at different scales. Scale invariantobject detection may improve the reliability and repeatability of theobject detection and may also use fewer number of clusterstructures—thus reduced memory resources and also lower computationalresources required to maintain fewer cluster structures.

FIG. 1S illustrates method 8020 for scale invariant object detection.

Method 8020 may include a first sequence of steps that may include step8022, 8024, 8026 and 8028.

Step 8022 may include receiving or generating a first image in which anobject appears in a first scale and a second image in which the objectappears in a second scale that differs from the first scale.

Step 8024 may include generating a first image signature and a secondimage signature.

The first image signature includes a first group of at least one certainfirst image identifier that identifies at least a part of the object.See, for example image 8000′ of FIG. 2A. The person is identified byidentifiers ID6 8016 and ID8 8018 that represent regions of interest8006 and 8008.

The second image signature includes a second group of certain secondimage identifiers that identify different parts of the object.

See, for example image 8000 of FIG. 19. The person is identified byidentifiers ID1 8011, ID2 8012, ID3 8013, and ID4 8014 that representregions of interest 8001, 8002, 8003 and 8004.

The second group is larger than first group—as the second group has moremembers than the first group.

Step 8026 may include linking between the at least one certain firstimage identifier and the certain second image identifiers.

Step 8026 may include linking between the first image signature, thesecond image signature and the object.

Step 8026 may include adding the first signature and the secondsignature to a certain concept structure that is associated with theobject. For example, referring to FIG. 1O, the signatures of the firstand second images may be included in a cluster concept out of4974(1)-4974(M).

Step 8028 may include determining whether an input image includes theobject based, at least in part, on the linking. The input image differsfrom the first and second images.

The determining may include determining that the input image includesthe object when a signature of the input image includes the at least onecertain first image identifier or the certain second image identifiers.

The determining may include determining that the input image includesthe object when the signature of the input image includes only a part ofthe at least one certain first image identifier or only a part of thecertain second image identifiers.

The linking may be performed for more than two images in which theobject appears in more than two scales.

For example, see FIG. 2B in which a person appears at three differentscales—at three different images.

In first image 8051 the person is included in a single region ofinterest 8061 and the signature 8051′ of first image 8051 includes anidentifier ID61 that identifies the single region of interest—identifiesthe person.

In second image 8052 the upper part of the person is included in regionof interest 8068, the lower part of the person is included in region ofinterest 8069 and the signature 8052′ of second image 8052 includesidentifiers ID68 and ID69 that identify regions of interest 8068 and8069 respectively.

In third image 8053 the eyes of the person are included in region ofinterest 8062, the mouth of the person is included in region of interest8063, the head of the person appears in region of interest 8064, theneck and arms of the person appear in region of interest 8065, themiddle part of the person appears in region of interest 8066, and thelower part of the person appears in region of interest 8067. Signature8053′ of third image 8053 includes identifiers ID62, ID63, ID64, ID65,ID55 and ID67 that identify regions of interest 8062-8067 respectively.

Method 8020 may link signatures 8051′, 8052′ and 8053′ to each other.For example—these signatures may be included in the same clusterstructure.

Method 8020 may link (i) ID61, (ii) signatures ID68 and ID69, and (ii)signature ID62, ID63, ID64, ID65, ID66 and ID67.

FIG. 2C illustrates method 8030 for object detection.

Method 8030 may include the steps of method 8020 or may be preceded bysteps 8022, 8024 and 8026.

Method 8030 may include a sequence of steps 8032, 8034, 8036 and 8038.

Step 8032 may include receiving or generating an input image.

Step 8034 may include generating a signature of the input image.

Step 8036 may include comparing the signature of the input image tosignatures of a certain concept structure. The certain concept structuremay be generated by method 8020.

Step 8038 may include determining that the input image comprises theobject when at least one of the signatures of the certain conceptstructure matches the signature of the input image.

FIG. 2D illustrates method 8040 for object detection.

Method 8040 may include the steps of method 8020 or may be preceded bysteps 8022, 8024 and 8026.

Method 8040 may include a sequence of steps 8041, 8043, 8045, 8047 and8049.

Step 8041 may include receiving or generating an input image.

Step 8043 may include generating a signature of the input image, thesignature of the input image comprises only some of the certain secondimage identifiers; wherein the input image of the second scale.

Step 8045 may include changing a scale of the input image to the firstscale to a provide an amended input image.

Step 8047 may include generating a signature of the amended input image.

Step 8049 may include verifying that the input image comprises theobject when the signature of the amended input image comprises the atleast one certain first image identifier.

FIG. 2E illustrates method 8050 for object detection.

Method 8050 may include the steps of method 8020 or may be preceded bysteps 8022, 8024 and 8026.

Method 8050 may include a sequence of steps 8052, 8054, 8056 and 8058.

Step 8052 may include receiving or generating an input image.

Step 8054 may include generating a signature of the input image.

Step 8056 may include searching in the signature of the input image forat least one of (a) the at least one certain first image identifier, and(b) the certain second image identifiers.

Step 8058 may include determining that the input image comprises theobject when the signature of the input image comprises the at least oneof (a) the at least one certain first image identifier, and (b) thecertain second image identifiers.

It should be noted that step 8056 may include searching in the signatureof the input image for at least one of (a) one or more certain firstimage identifier of the at least one certain first image identifier, and(b) at least one certain second image identifier of the certain secondimage identifiers.

It should be noted that step 8058 may include determining that the inputimage includes the object when the signature of the input imagecomprises the at least one of (a) one or more certain first imageidentifier of the at least one certain first image identifier, and (b)the at least one certain second image identifier.

Movement Based Bootstrapping

A single object may include multiple parts that are identified bydifferent identifiers of a signature of the image. In cases such asunsupervised learning, it may be beneficial to link the multiple objectparts to each other without receiving prior knowledge regarding theirinclusion in the object.

Additionally or alternatively, the linking can be done in order toverify a previous linking between the multiple object parts.

FIG. 2F illustrates method 8070 for object detection.

Method 8070 is for movement based object detection.

Method 8070 may include a sequence of steps 8071, 8073, 8075, 8077, 8078and 8079.

Step 8071 may include receiving or generating a video stream thatincludes a sequence of images.

Step 8073 may include generating image signatures of the images.

Each image is associated with an image signature that comprisesidentifiers. Each identifier identifiers a region of interest within theimage.

Step 8075 may include generating movement information indicative ofmovements of the regions of interest within consecutive images of thesequence of images. Step 8075 may be preceded by or may includegenerating or receiving location information indicative of a location ofeach region of interest within each image. The generating of themovement information is based on the location information.

Step 8077 may include searching, based on the movement information, fora first group of regions of interest that follow a first movement.Different first regions of interest are associated with different partsof an object.

Step 8078 may include linking between first identifiers that identifythe first group of regions of interest.

Step 8079 may include linking between first image signatures thatinclude the first linked identifiers.

Step 8079 may include adding the first image signatures to a firstconcept structure, the first concept structure is associated with thefirst image.

Step 8079 may be followed by determining whether an input image includesthe object based, at least in part, on the linking

An example of various steps of method 8070 is illustrated in FIG. 2H.

FIG. 2G illustrates three images 8091, 8092 and 8093 that were taken atdifferent points in time.

First image 8091 illustrates a gate 8089′ that is located in region ofinterest 8089 and a person that faces the gate. Various parts of theperson are located within regions of interest 8081, 8082, 8083, 8084 and8085.

The first image signature 8091′ includes identifiers ID81, ID82, ID83,ID84, ID85 and ID89 that identify regions of interest 8081, 8082, 8083,8084, 8085 and 8089 respectively.

The first image location information 8091″ includes the locations L81,L82, L83, L84, L85 and L89 of regions of interest 8081, 8082, 8083,8084, 8085 and 8089 respectively. A location of a region of interest mayinclude a location of the center of the region of interest, the locationof the borders of the region of interest or any location informationthat may define the location of the region of interest or a part of theregion of interest.

Second image 8092 illustrates a gate that is located in region ofinterest 8089 and a person that faces the gate. Various parts of theperson are located within regions of interest 8081, 8082, 8083, 8084 and8085. Second image also includes a pole that is located within region ofinterest 8086. In the first image that the pole was concealed by theperson.

The second image signature 8092′ includes identifiers ID81, ID82, ID83,ID84, ID85, ID86, and ID89 that identify regions of interest 8081, 8082,8083, 8084, 8085, 8086 and 8089 respectively.

The second image location information 8092″ includes the locations L81,L82, L83, L84, L85, L86 and L89 of regions of interest 8081, 8082, 8083,8084, 8085, 8086 and 8089 respectively.

Third image 8093 illustrates a gate that is located in region ofinterest 8089 and a person that faces the gate. Various parts of theperson are located within regions of interest 8081, 8082, 8083, 8084 and8085. Third image also includes a pole that is located within region ofinterest 8086, and a balloon that is located within region of interest8087.

The third image signature 8093′ includes identifiers ID81, ID82, ID83,ID84, ID85, ID86, ID87 and ID89 that identify regions of interest 8081,8082, 8083, 8084, 8085, 8086, 8087 and 8089 respectively.

The third image location information 8093″ includes the locations L81,L82, L83, L84, L85, L86, L87 and L89 of regions of interest 8081, 8082,8083, 8084, 8085, 8086, 8086 and 8089 respectively.

The motion of the various regions of interest may be calculated bycomparing the location information related to different images. Themovement information may take into account the different in theacquisition time of the images.

The comparison shows that regions of interest 8081, 8082, 8083, 8084,8085 move together and thus they should be linked to each other—and itmay be assumed that they all belong to the same object.

FIG. 2H illustrates method 8100 for object detection.

Method 8100 may include the steps of method 8070 or may be preceded bysteps 8071, 8073, 8075, 8077 and 8078.

Method 8100 may include the following sequence of steps:

-   -   Step 8102 of receiving or generating an input image.    -   Step 8104 of generating a signature of the input image.    -   Step 8106 of comparing the signature of the input image to        signatures of a first concept structure. The first concept        structure includes first identifiers that were linked to each        other based on movements of first regions of interest that are        identified by the first identifiers.    -   Step 8108 of determining that the input image includes a first        object when at least one of the signatures of the first concept        structure matches the signature of the input image.

FIG. 2I illustrates method 8110 for object detection.

Method 8110 may include the steps of method 8070 or may be preceded bysteps 8071, 8073, 8075, 8077 and 8078.

Method 8110 may include the following sequence of steps:

-   -   Step 8112 of receiving or generating an input image.    -   Step 8114 of generating a signature of the input image.    -   Step 8116 of searching in the signature of the input image for        at least one of the first identifiers.    -   Step 8118 of determining that the input image comprises the        object when the signature of the input image comprises at least        one of the first identifiers.

Object Detection that is Robust to Angle of Acquisition

Object detection may benefit from being robust to the angle ofacquisition—to the angle between the optical axis of an image sensor anda certain part of the object. This allows the detection process to bemore reliable, use fewer different clusters (may not require multipleclusters for identifying the same object from different images).

FIG. 2J illustrates method 8120 that includes the following steps:

-   -   Step 8122 of receiving or generating images of objects taken        from different angles.    -   Step 8124 of finding images of objects taken from different        angles that are close to each other. Close enough may be less        than 1,5,10,15 and 20 degrees—but the closeness may be better        reflected by the reception of substantially the same signature.    -   Step 8126 of linking between the images of similar signatures.        This may include searching for local similarities. The        similarities are local in the sense that they are calculated per        a subset of signatures. For example—assuming that the similarity        is determined per two images—then a first signature may be        linked to a second signature that is similar to the first image.        A third signature may be linked to the second image based on the        similarity between the second and third signatures- and even        regardless of the relationship between the first and third        signatures.

Step 8126 may include generating a concept data structure that includesthe similar signatures.

This so-called local or sliding window approach, in addition to theacquisition of enough images (that will statistically provide a largeangular coverage) will enable to generate a concept structure thatinclude signatures of an object taken at multiple directions.

FIG. 2K illustrates a person 8130 that is imaged from different angles(8131, 8132, 8133, 8134, 8135 and 8136). While the signature of a frontview of the person (obtained from angle 8131) differs from the signatureof the side view of the person (obtained from angle 8136), the signatureof images taken from multiple angles between angles 8141 and 8136compensates for the difference—as the difference between images obtainedfrom close angles are similar (local similarity) to each other.

Signature Tailored Matching Threshold

Object detection may be implemented by (a) receiving or generatingconcept structures that include signatures of media units and relatedmetadata, (b) receiving a new media unit, generating a new media unitsignature, and (c) comparing the new media unit signature to the conceptsignatures of the concept structures.

The comparison may include comparing new media unit signatureidentifiers (identifiers of objects that appear in the new media unit)to concept signature identifiers and determining, based on a signaturematching criteria whether the new media unit signature matches a conceptsignature. If such a match is found then the new media unit is regardedas including the object associated with that concept structure.

It was found that by applying an adjustable signature matching criteria,the matching process may be highly effective and may adapt itself to thestatistics of appearance of identifiers in different scenarios. Forexample—a match may be obtained when a relatively rear but highlydistinguishing identifier appears in the new media unit signature and ina cluster signature, but a mismatch may be declared when multiple commonand slightly distinguishing identifiers appear in the new media unitsignature and in a cluster signature.

FIG. 2L illustrates method 8200 for object detection.

Method 8200 may include:

-   -   Step 8210 of receiving an input image.    -   Step 8212 of generating a signature of the input image.    -   Step 8214 of comparing the signature of the input image to        signatures of a concept structure.    -   Step 8216 of determining whether the signature of the input        image matches any of the signatures of the concept structure        based on signature matching criteria, wherein each signature of        the concept structure is associated within a signature matching        criterion that is determined based on an object detection        parameter of the signature.    -   Step 8218 of concluding that the input image comprises an object        associated with the concept structure based on an outcome of the        determining.

The signature matching criteria may be a minimal number of matchingidentifiers that indicate of a match. For example—assuming a signaturethat include few tens of identifiers, the minimal number may varybetween a single identifier to all of the identifiers of the signature.

It should be noted that an input image may include multiple objects andthat an signature of the input image may match multiple clusterstructures. Method 8200 is applicable to all of the matching processes-and that the signature matching criteria may be set for each signatureof each cluster structure.

Step 8210 may be preceded by step 8202 of determining each signaturematching criterion by evaluating object detection capabilities of thesignature under different signature matching criteria.

Step 8202 may include:

-   -   Step 8203 of receiving or generating signatures of a group of        test images.    -   Step 8204 of calculating the object detection capability of the        signature, for each signature matching criterion of the        different signature matching criteria.    -   Step 8206 of selecting the signature matching criterion based on        the object detection capabilities of the signature under the        different signature matching criteria.

The object detection capability may reflect a percent of signatures ofthe group of test images that match the signature.

The selecting of the signature matching criterion comprises selectingthe signature matching criterion that once applied results in a percentof signatures of the group of test images that match the signature thatis closets to a predefined desired percent of signatures of the group oftest images that match the signature.

The object detection capability may reflect a significant change in thepercent of signatures of the group of test images that match thesignature. For example—assuming, that the signature matching criteria isa minimal number of matching identifiers and that changing the value ofthe minimal numbers may change the percentage of matching test images. Asubstantial change in the percentage (for example a change of more than10, 20, 30, 40 percent) may be indicative of the desired value. Thedesired value may be set before the substantial change, proximate to thesubstantial change, and the like.

For example, referring to FIG. 1O, cluster signatures CS(1,1), CS(2,5),CS(7,3) and CS(15,2) match unit signature 4972. Each of these matchesmay apply a unique signature matching criterion.

FIG. 2M illustrates method 8220 for object detection.

Method 8220 is for managing a concept structure.

Method 8220 may include:

-   -   Step 8222 of determining to add a new signature to the concept        structure.

The concept structure may already include at least one old signature.The new signature includes identifiers that identify at least parts ofobjects.

-   -   Step 8224 of determining a new signature matching criterion that        is based on one or more of the identifiers of the new signature.        The new signature matching criterion determines when another        signature matches the new signature. The determining of the new        signature matching criterion may include evaluating object        detection capabilities of the signature under different        signature matching criteria.

Step 8224 may include steps 8203, 8204 and 8206 (include din step 8206)of method 8200.

Examples of Systems

FIG. 22N illustrates an example of a system capable of executing one ormore of the mentioned above methods.

The system include various components, elements and/or units.

A component element and/or unit may be a processing circuitry may beimplemented as a central processing unit (CPU), and/or one or more otherintegrated circuits such as application-specific integrated circuits(ASICs), field programmable gate arrays (FPGAs), full-custom integratedcircuits, etc., or a combination of such integrated circuits.

Alternatively, each component element and/or unit may implemented inhardware, firmware, or software that may be executed by a processingcircuitry.

System 4900 may include sensing unit 4902, communication unit 4904,input 4911, processor 4950, and output 4919. The communication unit 4904may include the input and/or the output.

Input and/or output may be any suitable communications component such asa network interface card, universal serial bus (USB) port, disk reader,modem or transceiver that may be operative to use protocols such as areknown in the art to communicate either directly, or indirectly, withother elements of the system.

Processor 4950 may include at least some out of

-   -   Multiple spanning elements 4951(q).    -   Multiple merge elements 4952(r).    -   Object detector 4953.    -   Cluster manager 4954.    -   Controller 4955.    -   Selection unit 4956.    -   Object detection determination unit 4957.    -   Signature generator 4958.    -   Movement information unit 4959.    -   Identifier unit 4960.

There may be provided a method for low-power calculation of a signature,the method may include receiving or generating a media unit of multipleobjects; processing the media unit by performing multiple iterations,wherein at least some of the multiple iterations may include applying,by spanning elements of the iteration, dimension expansion process thatmay be followed by a merge operation; wherein the applying of thedimension expansion process of an iteration may include determining arelevancy of the spanning elements of the iteration; completing thedimension expansion process by relevant spanning elements of theiteration and reducing a power consumption of irrelevant spanningelements until, at least, a completion of the applying of the dimensionexpansion process; determining identifiers that may be associated withsignificant portions of an output of the multiple iterations; andproviding a signature that may include the identifiers and representsthe multiple objects.

The identifiers may be retrieval information for retrieving thesignificant portions.

The at least some of the multiple iterations may be a majority of themultiple iterations.

The output of the multiple iteration may include multiple propertyattributes for each segment out of multiple segments of the media unit;and wherein the significant portions of an output of the multipleiterations may include more impactful property attributes.

A first iteration of the multiple iteration may include applying thedimension expansion process by applying different filters on the mediaunit.

The at least some of the multiple iteration exclude at least a firstiteration of the multiple iterations.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iterationmay be performed by the spanning elements of the iteration.

The method may include a neural network processing operation that may beexecuted by one or more layers of a neural network and does not belongto the at least some of the multiple iterations.

The at least one iteration may be executed without reducing powerconsumption of irrelevant neurons of the one or more layers.

The one or more layers output information about properties of the mediaunit, wherein the information differs from a recognition of the multipleobjects.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative of an identity of the relevantspanning elements of the iteration.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative a history of dimension expansionprocesses until the iteration that differs from the first iteration.

Each spanning element may be associated with a subset of referenceidentifiers; and wherein the determining of the relevancy of eachspanning elements of the iteration may be based a relationship betweenthe subset of the reference identifiers of the spanning element and anoutput of a last merge operation before the iteration.

An output of a dimension expansion process of an iteration may be amultidimensional representation of the media unit that may include mediaunit regions of interest that may be associated with one or moreexpansion processes that generated the regions of interest.

A merge operation of the iteration may include selecting a subgroup ofmedia unit regions of interest based on a spatial relationship betweenthe subgroup of multidimensional regions of interest.

The method may include applying a merge function on the subgroup ofmultidimensional regions of interest.

The method may include applying an intersection function on the subgroupof multidimensional regions of interest.

A merge operation of the iteration may be based on an actual size of oneor more multidimensional regions of interest.

A merge operation of the iteration may be based on relationship betweensizes of the multidimensional regions of interest.

A merge operation of the iteration may be based on changes of the mediaunit regions of interest during at least the iteration and one or moreprevious iteration.

There may be provided a non-transitory computer readable medium forlow-power calculation of a signature, the non-transitory computerreadable medium may store instructions for receiving or generating amedia unit of multiple objects; processing the media unit by performingmultiple iterations, wherein at least some of the multiple iterationsmay include applying, by spanning elements of the iteration, dimensionexpansion process that may be followed by a merge operation; wherein theapplying of the dimension expansion process of an iteration may includedetermining a relevancy of the spanning elements of the iteration;completing the dimension expansion process by relevant spanning elementsof the iteration and reducing a power consumption of irrelevant spanningelements until, at least, a completion of the applying of the dimensionexpansion process; determining identifiers that may be associated withsignificant portions of an output of the multiple iterations; andproviding a signature that may include the identifiers and representsthe multiple objects.

The identifiers may be retrieval information for retrieving thesignificant portions.

The at least some of the multiple iterations may be a majority of themultiple iterations.

The output of the multiple iteration may include multiple propertyattributes for each segment out of multiple segments of the media unit;and wherein the significant portions of an output of the multipleiterations may include more impactful property attributes.

A first iteration of the multiple iteration may include applying thedimension expansion process by applying different filters on the mediaunit.

The at least some of the multiple iteration exclude at least a firstiteration of the multiple iterations.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iterationmay be performed by the spanning elements of the iteration.

The non-transitory computer readable medium may store instructions forperforming a neural network processing operation, by one or more layersof a neural network, wherein the neural network processing operation anddoes not belong to the at least some of the multiple iterations.

The non-transitory computer readable medium the at least one iterationmay be executed without reducing power consumption of irrelevant neuronsof the one or more layers.

The one or more layers output information about properties of the mediaunit, wherein the information differs from a recognition of the multipleobjects.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative of an identity of the relevantspanning elements of the iteration.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative a history of dimension expansionprocesses until the iteration that differs from the first iteration.

Each spanning element may be associated with a subset of referenceidentifiers; and wherein the determining of the relevancy of eachspanning elements of the iteration may be based a relationship betweenthe subset of the reference identifiers of the spanning element and anoutput of a last merge operation before the iteration.

An output of a dimension expansion process of an iteration may be amultidimensional representation of the media unit that may include mediaunit regions of interest that may be associated with one or moreexpansion processes that generated the regions of interest.

A merge operation of the iteration may include selecting a subgroup ofmedia unit regions of interest based on a spatial relationship betweenthe subgroup of multidimensional regions of interest.

The non-transitory computer readable medium may store instructions forapplying a merge function on the subgroup of multidimensional regions ofinterest.

The non-transitory computer readable medium may store instructions forapplying an intersection function on the subgroup of multidimensionalregions of interest.

A merge operation of the iteration may be based on an actual size of oneor more multidimensional regions of interest.

A merge operation of the iteration may be based on relationship betweensizes of the multidimensional regions of interest.

A merge operation of the iteration may be based on changes of the mediaunit regions of interest during at least the iteration and one or moreprevious iteration.

There may be provided a signature generator that may include an inputthat may be configured to receive or generate a media unit of multipleobjects; an processor that may be configured to process the media unitby performing multiple iterations, wherein at least some of the multipleiterations may include applying, by spanning elements of the iteration,dimension expansion process that may be followed by a merge operation;wherein the applying of the dimension expansion process of an iterationmay include determining a relevancy of the spanning elements of theiteration; completing the dimension expansion process by relevantspanning elements of the iteration and reducing a power consumption ofirrelevant spanning elements until, at least, a completion of theapplying of the dimension expansion process; an identifier unit that maybe configured to determine identifiers that may be associated withsignificant portions of an output of the multiple iterations; and anoutput that may be configured to provide a signature that may includethe identifiers and represents the multiple objects.

The identifiers may be retrieval information for retrieving thesignificant portions.

The at least some of the multiple iterations may be a majority of themultiple iterations.

The output of the multiple iteration may include multiple propertyattributes for each segment out of multiple segments of the media unit;and wherein the significant portions of an output of the multipleiterations may include more impactful property attributes.

A first iteration of the multiple iteration may include applying thedimension expansion process by applying different filters on the mediaunit.

The at least some of the multiple iteration exclude at least a firstiteration of the multiple iterations.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iterationmay be performed by the spanning elements of the iteration.

The signature generator that may include one or more layers of a neuralnetwork that may be configured to perform a neural network processingoperation, wherein the neural network processing operation does notbelong to the at least some of the multiple iterations.

At least one iteration may be executed without reducing powerconsumption of irrelevant neurons of the one or more layers.

The one or more layers output information about properties of the mediaunit, wherein the information differs from a recognition of the multipleobjects.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative of an identity of the relevantspanning elements of the iteration.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative a history of dimension expansionprocesses until the iteration that differs from the first iteration.

Each spanning element may be associated with a subset of referenceidentifiers; and wherein the determining of the relevancy of eachspanning elements of the iteration may be based a relationship betweenthe subset of the reference identifiers of the spanning element and anoutput of a last merge operation before the iteration.

An output of a dimension expansion process of an iteration may be amultidimensional representation of the media unit that may include mediaunit regions of interest that may be associated with one or moreexpansion processes that generated the regions of interest.

A merge operation of the iteration may include selecting a subgroup ofmedia unit regions of interest based on a spatial relationship betweenthe subgroup of multidimensional regions of interest.

The signature generator that may be configured to apply a merge functionon the subgroup of multidimensional regions of interest.

The signature generator that may be configured to apply an intersectionfunction on the subgroup of multidimensional regions of interest.

A merge operation of the iteration may be based on an actual size of oneor more multidimensional regions of interest.

A merge operation of the iteration may be based on relationship betweensizes of the multidimensional regions of interest.

A merge operation of the iteration may be based on changes of the mediaunit regions of interest during at least the iteration and one or moreprevious iteration.

There may be provided There may be provided a method for low-powercalculation of a signature of a media unit by a group of calculatingelements, the method may include calculating, multiple attributes ofsegments of the media unit; wherein the calculating may includedetermining, by each calculating element of multiple calculatingelements, a relevancy of the calculation unit to the media unit toprovide irrelevant calculating elements and relevant calculatingelements; reducing a power consumption of each irrelevant calculatingelement; and completing the calculating of the multiple attributes ofsegments of the media unit by the relevant calculating elements;determining identifiers that may be associated with significantattributes out of the multiple attributes of segments of the media unit;and providing a signature that may include the identifiers andrepresents the multiple objects.

The calculating elements may be spanning elements.

Each calculating element may be associated with a subset of one or morereference identifiers; and wherein the determining of a relevancy of thecalculation unit to the media unit may be based on a relationshipbetween the subset and the identifiers related to the media unit.

Each calculating element may be associated with a subset of one or morereference identifiers; and wherein a calculation element may be relevantto the media unit when the identifiers related to the media unit mayinclude each reference identifier of the subset.

The calculating of the multiple attributes of segments of the media unitmay be executed in multiple iterations; and wherein each iteration maybe executed by calculation elements associated with the iteration;wherein the determining, by each calculating element of multiplecalculating elements, of the relevancy of the calculation unit to themedia unit may be executed per iteration.

The multiple iterations may be preceded by a calculation of initialmedia unit attributes by one or more layers of a neural network.

There may be provided a non-transitory computer readable medium forlow-power calculation of a signature of a media unit by a group ofcalculating elements, the non-transitory computer readable medium maystore instructions for calculating, multiple attributes of segments ofthe media unit; wherein the calculating may include determining, by eachcalculating element of multiple calculating elements, a relevancy of thecalculation unit to the media unit to provide irrelevant calculatingelements and relevant calculating elements; reducing a power consumptionof each irrelevant calculating element; and completing the calculatingof the multiple attributes of segments of the media unit by the relevantcalculating elements; determining identifiers that may be associatedwith significant attributes out of the multiple attributes of segmentsof the media unit; and providing a signature that may include theidentifiers and represents the multiple objects.

The calculating elements may be spanning elements.

Each calculating element may be associated with a subset of one or morereference identifiers; and wherein the determining of a relevancy of thecalculation unit to the media unit may be based on a relationshipbetween the subset and the identifiers related to the media unit.

Each calculating element may be associated with a subset of one or morereference identifiers; and wherein a calculation element may be relevantto the media unit when the identifiers related to the media unit mayinclude each reference identifier of the subset.

The calculating of the multiple attributes of segments of the media unitmay be executed in multiple iterations; and wherein each iteration maybe executed by calculation elements associated with the iteration;wherein the determining, by each calculating element of multiplecalculating elements, of the relevancy of the calculation unit to themedia unit may be executed per iteration.

The multiple iterations may be preceded by a calculation of initialmedia unit attributes by one or more layers of a neural network.

There may be provided a signature generator that may include a processorthat may be configured to calculate multiple attributes of segments ofthe media unit; wherein the calculating may include determining, by eachcalculating element of multiple calculating elements of the processor, arelevancy of the calculation unit to the media unit to provideirrelevant calculating elements and relevant calculating elements;reducing a power consumption of each irrelevant calculating element; andcompleting the calculating of the multiple attributes of segments of themedia unit by the relevant calculating elements; an identifier unit thatmay be configured to determine identifiers that may be associated withsignificant attributes out of the multiple attributes of segments of themedia unit; and an output that may be configured to provide a signaturethat may include the identifiers and represents the multiple objects.

The calculating elements may be spanning elements.

Each calculating element may be associated with a subset of one or morereference identifiers; and wherein the determining of a relevancy of thecalculation unit to the media unit may be based on a relationshipbetween the subset and the identifiers related to the media unit.

Each calculating element may be associated with a subset of one or morereference identifiers; and wherein a calculation element may be relevantto the media unit when the identifiers related to the media unit mayinclude each reference identifier of the subset.

The calculating of the multiple attributes of segments of the media unitmay be executed in multiple iterations; and wherein each iteration maybe executed by calculation elements associated with the iteration;wherein the determining, by each calculating element of multiplecalculating elements, of the relevancy of the calculation unit to themedia unit may be executed per iteration.

The multiple iterations may be preceded by a calculation of initialmedia unit attributes by one or more layers of a neural network.

There may be provided a method for generating a hybrid representation ofa media unit, the method may include receiving or generating the mediaunit; processing the media unit by performing multiple iterations,wherein at least some of the multiple iterations may include applying,by spanning elements of the iteration, dimension expansion process thatmay be followed by a merge operation; selecting, based on an output ofthe multiple iterations, media unit regions of interest that contributedto the output of the multiple iterations; and providing the hybridrepresentation, wherein the hybrid representation may include shapeinformation regarding shapes of the media unit regions of interest, anda media unit signature that may include identifiers that identify themedia unit regions of interest.

The selecting of the media regions of interest may be executed persegment out of multiple segments of the media unit.

The shape information may include polygons that represent shapes thatsubstantially bound the media unit regions of interest.

The providing of the hybrid representation of the media unit may includecompressing the shape information of the media unit to providecompressed shape information of the media unit.

The method may include comparing the media unit signature of the mediaunit to signatures of multiple concept structures to find a matchingconcept structure that has at least one matching signature that matchesto the media unit signature; and calculating higher accuracy shapeinformation that may be related to regions of interest of the mediaunit, wherein the higher accuracy shape information may be of higheraccuracy than the compressed shape information of the media unit,wherein the calculating may be based on shape information associatedwith at least some of the matching signatures.

The method may include determining shapes of the media unit regions ofinterest using the higher accuracy shape information.

For each media unit region of interest, the calculating of the higheraccuracy shape information may include virtually overlaying shapes ofcorresponding media units of interest of at least some of the matchingsignatures.

There may be provided a non-transitory computer readable medium forgenerating a hybrid representation of a media unit, the non-transitorycomputer readable medium may store instructions for receiving orgenerating the media unit; processing the media unit by performingmultiple iterations, wherein at least some of the multiple iterationsmay include applying, by spanning elements of the iteration, dimensionexpansion process that may be followed by a merge operation; selecting,based on an output of the multiple iterations, media unit regions ofinterest that contributed to the output of the multiple iterations; andproviding the hybrid representation, wherein the hybrid representationmay include shape information regarding shapes of the media unit regionsof interest, and a media unit signature that may include identifiersthat identify the media unit regions of interest.

The selecting of the media regions of interest may be executed persegment out of multiple segments of the media unit.

The shape information may include polygons that represent shapes thatsubstantially bound the media unit regions of interest.

The providing of the hybrid representation of the media unit may includecompressing the shape information of the media unit to providecompressed shape information of the media unit.

The non-transitory computer readable medium may store instructions forcomparing the media unit signature of the media unit to signatures ofmultiple concept structures to find a matching concept structure thathas at least one matching signature that matches to the media unitsignature; and calculating higher accuracy shape information that may berelated to regions of interest of the media unit, wherein the higheraccuracy shape information may be of higher accuracy than the compressedshape information of the media unit, wherein the calculating may bebased on shape information associated with at least some of the matchingsignatures.

The non-transitory computer readable medium may store instructions fordetermining shapes of the media unit regions of interest using thehigher accuracy shape information.

The for each media unit region of interest, the calculating of thehigher accuracy shape information may include virtually overlayingshapes of corresponding media units of interest of at least some of thematching signatures.

There may be provided a hybrid representation generator for generating ahybrid representation of a media unit, the hybrid representationgenerator may include an input that may be configured to receive orgenerate the media unit; a processor that may be configured to processthe media unit by performing multiple iterations, wherein at least someof the multiple iterations may include applying, by spanning elements ofthe iteration, dimension expansion process that may be followed by amerge operation; a selection unit that may be configured to select,based on an output of the multiple iterations, media unit regions ofinterest that contributed to the output of the multiple iterations; andan output that may be configured to provide the hybrid representation,wherein the hybrid representation may include shape informationregarding shapes of the media unit regions of interest, and a media unitsignature that may include identifiers that identify the media unitregions of interest.

The selecting of the media regions of interest may be executed persegment out of multiple segments of the media unit.

The shape information may include polygons that represent shapes thatsubstantially bound the media unit regions of interest.

The hybrid representation generator that may be configured to compressthe shape information of the media unit to provide compressed shapeinformation of the media unit.

There may be provided a method for scale invariant object detection, themethod may include receiving or generating a first image in which anobject appears in a first scale and a second image in which the objectappears in a second scale that differs from the first scale; generatinga first image signature and a second image signature; wherein the firstimage signature may include a first group of at least one certain firstimage identifier that identifies at least a part of the object; whereinthe second image signature may include a second group of certain secondimage identifiers that identify different parts of the object; whereinthe second group may be larger than first group; and linking between theat least one certain first image identifier and the certain second imageidentifiers.

The method may include linking between the first image signature, thesecond image signature and the object.

The linking may include adding the first signature and the secondsignature to a certain concept structure that may be associated with theobject.

The method may include receiving or generating an input image;generating a signature of the input image; comparing the signature ofthe input image to signatures of the certain concept structure; anddetermining that the input image may include the object when at leastone of the signatures of the certain concept structure matches thesignature of the input image.

The method may include receiving or generating an input image;generating a signature of the input image, the signature of the inputimage may include only some of the certain second image identifiers;wherein the input image of the second scale; changing a scale of theinput image to the first scale to a provide an amended input image;generating a signature of the amended input image; and verifying thatthe input image may include the object when the signature of the amendedinput image may include the at least one certain first image identifier.

The method may include receiving or generating an input image;generating a signature of the input image; searching in the signature ofthe input image for at least one of (a) the at least one certain firstimage identifier, and (b) the certain second image identifiers; anddetermining that the input image may include the object when thesignature of the input image may include the at least one of (a) the atleast one certain first image identifier, and (b) the certain secondimage identifiers.

The method may include receiving or generating an input image;generating a signature of the input image; searching in the signature ofthe input image for at least one of (a) one or more certain first imageidentifier of the at least one certain first image identifier, and (b)at least one certain second image identifier of the certain second imageidentifiers; and determining that a input image includes the object whenthe signature of the input image may include the at least one of (a) oneor more certain first image identifier of the at least one certain firstimage identifier, and (b) the at least one certain second imageidentifier.

There may be provided a non-transitory computer readable medium forscale invariant object detection, the non-transitory computer readablemedium may store instructions for receiving or generating a first imagein which an object appears in a first scale and a second image in whichthe object appears in a second scale that differs from the first scale;generating a first image signature and a second image signature; whereinthe first image signature may include a first group of at least onecertain first image identifier that identifies at least a part of theobject; wherein the second image signature may include a second group ofcertain second image identifiers that identify different parts of theobject; wherein the second group may be larger than first group; andlinking between the at least one certain first image identifier and thecertain second image identifiers.

The non-transitory computer readable medium may store instructions forlinking between the first image signature, the second image signatureand the object.

The linking may include adding the first signature and the secondsignature to a certain concept structure that may be associated with theobject.

The non-transitory computer readable medium may store instructions forreceiving or generating an input image; generating a signature of theinput image; comparing the signature of the input image to signatures ofthe certain concept structure; and determining that the input image mayinclude the object when at least one of the signatures of the certainconcept structure matches the signature of the input image.

The non-transitory computer readable medium may store instructions forreceiving or generating an input image; generating a signature of theinput image, the signature of the input image may include only some ofthe certain second image identifiers; wherein the input image of thesecond scale; changing a scale of the input image to the first scale toa provide an amended input image; generating a signature of the amendedinput image; and verifying that the input image may include the objectwhen the signature of the amended input image may include the at leastone certain first image identifier.

The non-transitory computer readable medium may store instructions forreceiving or generating an input image; generating a signature of theinput image; searching in the signature of the input image for at leastone of (a) the at least one certain first image identifier, and (b) thecertain second image identifiers; and determining that a input imageincludes the object when the signature of the input image may includethe at least one of (a) the at least one certain first image identifier,and (b) the certain second image identifiers.

The non-transitory computer readable medium may store instructions forreceiving or generating an input image; generating a signature of theinput image; searching in the signature of the input image for at leastone of (a) one or more certain first image identifier of the at leastone certain first image identifier, and (b) at least one certain secondimage identifier of the certain second image identifiers; anddetermining that a input image includes the object when the signature ofthe input image may include the at least one of (a) one or more certainfirst image identifier of the at least one certain first imageidentifier, and (b) the at least one certain second image identifier.

There may be provided an object detector for scale invariant objectdetection, that may include an input that may be configured to receive afirst image in which an object appears in a first scale and a secondimage in which the object appears in a second scale that differs fromthe first scale; a signature generator that may be configured togenerate a first image signature and a second image signature; whereinthe first image signature may include a first group of at least onecertain first image identifier that identifies at least a part of theobject; wherein the second image signature may include a second group ofcertain second image identifiers that identify different parts of theobject; wherein the second group may be larger than first group; and anobject detection determination unit that may be configured to linkbetween the at least one certain first image identifier and the certainsecond image identifiers.

The object detection determination unit may be configured to linkbetween the first image signature, the second image signature and theobject.

The object detection determination unit may be configured to add thefirst signature and the second signature to a certain concept structurethat may be associated with the object.

The input may be configured to receive an input image; wherein thesignal generator may be configured to generate a signature of the inputimage; wherein the object detection determination unit may be configuredto compare the signature of the input image to signatures of the certainconcept structure, and to determine that the input image may include theobject when at least one of the signatures of the certain conceptstructure matches the signature of the input image.

The input may be configured to receive an input image; wherein thesignature generator may be configured to generate a signature of theinput image, the signature of the input image may include only some ofthe certain second image identifiers; wherein the input image of thesecond scale; wherein the input may be configured to receive an amendedinput image that was generated by changing a scale of the input image tothe first scale; wherein the signature generator may be configured togenerate a signature of the amended input image; and wherein the objectdetection determination unit may be configured to verify that the inputimage may include the object when the signature of the amended inputimage may include the at least one certain first image identifier.

The input may be configured to receive an input image; wherein thesignature generator may be configured to generate a signature of theinput image; wherein the object detection determination unit may beconfigured to search in the signature of the input image for at leastone of (a) the at least one certain first image identifier, and (b) thecertain second image identifiers; and determine that a input imageincludes the object when the signature of the input image may includethe at least one of (a) the at least one certain first image identifier,and (b) the certain second image identifiers.

The input may be configured to receive an input image; wherein thesignature generator may be configured to generate a signature of theinput image; wherein the object detection determination unit may beconfigured to search in the signature of the input image for at leastone of (a) one or more certain first image identifier of the at leastone certain first image identifier, and (b) at least one certain secondimage identifier of the certain second image identifiers; and determinethat a input image includes the object when the signature of the inputimage may include the at least one of (a) one or more certain firstimage identifier of the at least one certain first image identifier, and(b) the at least one certain second image identifier.

There may be provided a method for movement based object detection, themethod may include receiving or generating a video stream that mayinclude a sequence of images; generating image signatures of the images;wherein each image may be associated with an image signature that mayinclude identifiers; wherein each identifier identifiers a region ofinterest within the image; generating movement information indicative ofmovements of the regions of interest within consecutive images of thesequence of images; searching, based on the movement information, for afirst group of regions of interest that follow a first movement; whereindifferent first regions of interest may be associated with differentparts of an object; and linking between first identifiers that identifythe first group of regions of interest.

The linking may include linking between first image signatures thatinclude the first linked identifiers.

The linking may include adding the first image signatures to a firstconcept structure, the first concept structure may be associated withthe first image.

The method may include receiving or generating an input image;generating a signature of the input image; comparing the signature ofthe input image to signatures of the first concept structure; anddetermining that the input image may include a first object when atleast one of the signatures of the first concept structure matches thesignature of the input image.

The method may include receiving or generating an input image;generating a signature of the input image; searching in the signature ofthe input image for at least one of the first identifiers; anddetermining that the input image may include the object when thesignature of the input image may include at least one of the firstidentifiers.

The method may include generating location information indicative of alocation of each region of interest within each image; wherein thegenerating movement information may be based on the locationinformation.

There may be provided a non-transitory computer readable medium formovement based object detection, the non-transitory computer readablemedium may include receiving or generating a video stream that mayinclude a sequence of images; generating image signatures of the images;wherein each image may be associated with an image signature that mayinclude identifiers; wherein each identifier identifiers a region ofinterest within the image; wherein different region of interests includedifferent objects; generating movement information indicative ofmovements of the regions of interest within consecutive images of thesequence of images; searching, based on the movement information, for afirst group of regions of interest that follow a first movement; andlinking between first identifiers that identify the first group ofregions of interest.

The linking may include linking between first image signatures thatinclude the first linked identifiers.

The linking may include adding the first image signatures to a firstconcept structure, the first concept structure may be associated withthe first image.

The non-transitory computer readable medium may store instructions forreceiving or generating an input image; generating a signature of theinput image; comparing the signature of the input image to signatures ofthe first concept structure; and determining that the input image mayinclude a first object when at least one of the signatures of the firstconcept structure matches the signature of the input image.

The non-transitory computer readable medium may store instructions forreceiving or generating an input image; generating a signature of theinput image; searching in the signature of the input image for at leastone of the first identifiers; and determining that the input image mayinclude the object when the signature of the input image may include atleast one of the first identifiers.

The non-transitory computer readable medium may store instructions forgenerating location information indicative of a location of each regionof interest within each image; wherein the generating movementinformation may be based on the location information.

There may be provided an object detector that may include an input thatmay be configured to receive a video stream that may include a sequenceof images; a signature generator that may be configured to generateimage signatures of the images; wherein each image may be associatedwith an image signature that may include identifiers; wherein eachidentifier identifiers a region of interest within the image; a movementinformation unit that may be is configured to generate movementinformation indicative of movements of the regions of interest withinconsecutive images of the sequence of images; an object detectiondetermination unit that may be configured to search, based on themovement information, for a first group of regions of interest thatfollow a first movement; wherein different first regions of interest maybe associated with different parts of an object; and link between firstidentifiers that identify the first group of regions of interest.

The linking may include linking between first image signatures thatinclude the first linked identifiers.

The linking may include adding the first image signatures to a firstconcept structure, the first concept structure may be associated withthe first image.

The input may be configured to receive an input image; wherein thesignature generator may be configured to generate a signature of theinput image; and wherein the object detection determination unit may beconfigured to compare the signature of the input image to signatures ofthe first concept structure; and to determine that the input image mayinclude a first object when at least one of the signatures of the firstconcept structure matches the signature of the input image.

The input may be configured to receive an input image; wherein thesignature generator may be configured to generate a signature of theinput image; and wherein the object detection determination unit may beconfigured to search in the signature of the input image for at leastone of the first identifiers; and to determine that the input image mayinclude the object when the signature of the input image may include atleast one of the first identifiers.

The object detector that may be configured to generate locationinformation indicative of a location of each region of interest withineach image; wherein the generating of the movement information may bebased on the location information.

There may be provided a method for object detection, the method mayinclude receiving an input image; generating a signature of the inputimage; comparing the signature of the input image to signatures of aconcept structure; determining whether the signature of the input imagematches any of the signatures of the concept structure based onsignature matching criteria, wherein each signature of the conceptstructure may be associated within a signature matching criterion thatmay be determined based on an object detection parameter of thesignature; and concluding that the input image may include an objectassociated with the concept structure based on an outcome of thedetermining.

Each signature matching criterion may be determined by evaluating objectdetection capabilities of the signature under different signaturematching criteria.

The evaluating of the object detection capabilities of the signatureunder different signature matching criteria may include receiving orgenerating signatures of a group of test images; calculating the objectdetection capability of the signature, for each signature matchingcriterion of the different signature matching criteria; and selectingthe signature matching criterion based on the object detectioncapabilities of the signature under the different signature matchingcriteria.

The object detection capability reflects a percent of signatures of thegroup of test images that match the signature.

The selecting of the signature matching criterion may include selectingthe signature matching criterion that one applied results in a percentof signatures of the group of test images that match the signature thatmay be closets to a predefined desired percent of signatures of thegroup of test images that match the signature.

The signature matching criteria may be a minimal number of matchingidentifiers that indicate of a match.

There may be provided a method for managing a concept structure, themethod may include determining to add a new signature to the conceptstructure, wherein the concept structure already may include at leastone old signature; wherein the new signature may include identifiersthat identify at least parts of objects; and determining a new signaturematching criterion that may be based on one or more of the identifiersof the new signature; wherein the new signature matching criteriondetermines when another signature matches the new signature; wherein thedetermining of the new signature matching criterion may includeevaluating object detection capabilities of the signature underdifferent signature matching criteria.

The evaluating of the object detection capabilities of the signatureunder different signature matching criteria may include receiving orgenerating signatures of a group of test images; calculating the objectdetection capability of the signature, for each signature matchingcriterion of the different signature matching criteria; and selectingthe signature matching criterion based on the object detectioncapabilities of the signature under the different signature matchingcriteria.

The object detection capability reflects a percent of signatures of thegroup of test images that match the signature.

The selecting of the signature matching criterion may include selectingthe signature matching criterion that one applied results in a percentof signatures of the group of test images that match the signature thatmay be closets to a predefined desired percent of signatures of thegroup of test images that match the signature.

The signature matching criteria may be a minimal number of matchingidentifiers that indicate of a match.

There may be provided a non-transitory computer readable medium forobject detection, the non-transitory computer readable medium may storeinstructions for receiving an input image; generating a signature of theinput image; comparing the signature of the input image to signatures ofa concept structure; determining whether the signature of the inputimage matches any of the signatures of the concept structure based onsignature matching criteria, wherein each signature of the conceptstructure may be associated within a signature matching criterion thatmay be determined based on an object detection parameter of thesignature; and concluding that the input image may include an objectassociated with the concept structure based on an outcome of thedetermining.

Each signature matching criterion may be determined by evaluating objectdetection capabilities of the signature under different signaturematching criteria.

The evaluating of the object detection capabilities of the signatureunder different signature matching criteria may include receiving orgenerating signatures of a group of test images; calculating the objectdetection capability of the signature, for each signature matchingcriterion of the different signature matching criteria; and selectingthe signature matching criterion based on the object detectioncapabilities of the signature under the different signature matchingcriteria.

The object detection capability reflects a percent of signatures of thegroup of test images that match the signature.

The selecting of the signature matching criterion may include selectingthe signature matching criterion that one applied results in a percentof signatures of the group of test images that match the signature thatmay be closets to a predefined desired percent of signatures of thegroup of test images that match the signature.

The signature matching criteria may be a minimal number of matchingidentifiers that indicate of a match.

There may be provided a non-transitory computer readable medium formanaging a concept structure, the non-transitory computer readablemedium may store instructions for determining to add a new signature tothe concept structure, wherein the concept structure already may includeat least one old signature; wherein the new signature may includeidentifiers that identify at least parts of objects; and determining anew signature matching criterion that may be based on one or more of theidentifiers of the new signature; wherein the new signature matchingcriterion determines when another signature matches the new signature;wherein the determining of the new signature matching criterion mayinclude evaluating object detection capabilities of the signature underdifferent signature matching criteria.

The evaluating of the object detection capabilities of the signatureunder different signature matching criteria may include receiving orgenerating signatures of a group of test images; calculating the objectdetection capability of the signature, for each signature matchingcriterion of the different signature matching criteria; and selectingthe signature matching criterion based on the object detectioncapabilities of the signature under the different signature matchingcriteria.

The object detection capability reflects a percent of signatures of thegroup of test images that match the signature.

The selecting of the signature matching criterion may include selectingthe signature matching criterion that one applied results in a percentof signatures of the group of test images that match the signature thatmay be closets to a predefined desired percent of signatures of thegroup of test images that match the signature.

The signature matching criteria may be a minimal number of matchingidentifiers that indicate of a match.

There may be provided an object detector that may include an input thatmay be configured to receive an input image; a signature generator thatmay be configured to generate a signature of the input image; an objectdetection determination unit that may be configured to compare thesignature of the input image to signatures of a concept structure;determine whether the signature of the input image matches any of thesignatures of the concept structure based on signature matchingcriteria, wherein each signature of the concept structure may beassociated within a signature matching criterion that may be determinedbased on an object detection parameter of the signature; and concludethat the input image may include an object associated with the conceptstructure based on an outcome of the determining.

The object detector according to claim, may include a signature matchingcriterion unit that may be configured to determine each signaturematching criterion by evaluating object detection capabilities of thesignature under different signature matching criteria.

The input may be configured to receive signatures of a group of testimages; wherein the signature matching criterion unit may be configuredto calculate the object detection capability of the signature, for eachsignature matching criterion of the different signature matchingcriteria; and select the signature matching criterion based on theobject detection capabilities of the signature under the differentsignature matching criteria.

The object detector according to claim wherein the object detectioncapability reflects a percent of signatures of the group of test imagesthat match the signature.

The object detector according to claim wherein the signature matchingcriterion unit may be configured to select the signature matchingcriterion that one applied results in a percent of signatures of thegroup of test images that match the signature that may be closets to apredefined desired percent of signatures of the group of test imagesthat match the signature.

The object detector according to claim wherein the signature matchingcriteria may be a minimal number of matching identifiers that indicateof a match.

There may be provided a concept structure manager that may include acontroller that may be configured to determine to add a new signature tothe concept structure, wherein the concept structure already may includeat least one old signature; wherein the new signature may includeidentifiers that identify at least parts of objects; and a signaturematching criterion unit that may be configured to determine a newsignature matching criterion that may be based on one or more of theidentifiers of the new signature; wherein the new signature matchingcriterion determines when another signature matches the new signature;wherein the determining of the new signature matching criterion mayinclude evaluating object detection capabilities of the signature underdifferent signature matching criteria.

The signature matching criterion unit may be configured to determineeach signature matching criterion by evaluating object detectioncapabilities of the signature under different signature matchingcriteria.

The input may be configured to receive signatures of a group of testimages; wherein the signature matching criterion unit may be configuredto calculate the object detection capability of the signature, for eachsignature matching criterion of the different signature matchingcriteria; and select the signature matching criterion based on theobject detection capabilities of the signature under the differentsignature matching criteria.

The concept manager according to claim wherein the object detectioncapability reflects a percent of signatures of the group of test imagesthat match the signature.

The concept manager according to claim wherein the signature matchingcriterion unit may be configured to select the signature matchingcriterion that one applied results in a percent of signatures of thegroup of test images that match the signature that may be closets to apredefined desired percent of signatures of the group of test imagesthat match the signature.

The concept manager according to claim wherein the signature matchingcriteria may be a minimal number of matching identifiers that indicateof a match.

While the foregoing written description of the invention enables one ofordinary skill to make and use what is considered presently to be thebest mode thereof, those of ordinary skill will understand andappreciate the existence of variations, combinations, and equivalents ofthe specific embodiment, method, and examples herein. The inventionshould therefore not be limited by the above described embodiment,method, and examples, but by all embodiments and methods within thescope and spirit of the invention as claimed.

In the foregoing specification, the invention has been described withreference to specific examples of embodiments of the invention. It will,however, be evident that various modifications and changes may be madetherein without departing from the broader spirit and scope of theinvention as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under”and the like in the description and in the claims, if any, are used fordescriptive purposes and not necessarily for describing permanentrelative positions. It is understood that the terms so used areinterchangeable under appropriate circumstances such that theembodiments of the invention described herein are, for example, capableof operation in other orientations than those illustrated or otherwisedescribed herein.

Furthermore, the terms “assert” or “set” and “negate” (or “deassert” or“clear”) are used herein when referring to the rendering of a signal,status bit, or similar apparatus into its logically true or logicallyfalse state, respectively. If the logically true state is a logic levelone, the logically false state is a logic level zero. And if thelogically true state is a logic level zero, the logically false state isa logic level one.

Those skilled in the art will recognize that the boundaries betweenlogic blocks are merely illustrative and that alternative embodimentsmay merge logic blocks or circuit elements or impose an alternatedecomposition of functionality upon various logic blocks or circuitelements. Thus, it is to be understood that the architectures depictedherein are merely exemplary, and that in fact many other architecturesmay be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality may be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundariesbetween the above described operations merely illustrative. The multipleoperations may be combined into a single operation, a single operationmay be distributed in additional operations and operations may beexecuted at least partially overlapping in time. Moreover, alternativeembodiments may include multiple instances of a particular operation,and the order of operations may be altered in various other embodiments.

Also for example, in one embodiment, the illustrated examples may beimplemented as circuitry located on a single integrated circuit orwithin a same device. Alternatively, the examples may be implemented asany number of separate integrated circuits or separate devicesinterconnected with each other in a suitable manner.

However, other modifications, variations and alternatives are alsopossible. The specifications and drawings are, accordingly, to beregarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘comprising’ does notexclude the presence of other elements or steps then those listed in aclaim. Furthermore, the terms “a” or “an,” as used herein, are definedas one or more than one. Also, the use of introductory phrases such as“at least one” and “one or more” in the claims should not be construedto imply that the introduction of another claim element by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim element to inventions containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an.”The same holds true for the use of definite articles. Unless statedotherwise, terms such as “first” and “second” are used to arbitrarilydistinguish between the elements such terms describe. Thus, these termsare not necessarily intended to indicate temporal or otherprioritization of such elements. The mere fact that certain measures arerecited in mutually different claims does not indicate that acombination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

It is appreciated that various features of the embodiments of thedisclosure which are, for clarity, described in the contexts of separateembodiments may also be provided in combination in a single embodiment.Conversely, various features of the embodiments of the disclosure whichare, for brevity, described in the context of a single embodiment mayalso be provided separately or in any suitable sub-combination.

It will be appreciated by persons skilled in the art that theembodiments of the disclosure are not limited by what has beenparticularly shown and described hereinabove. Rather the scope of theembodiments of the disclosure is defined by the appended claims andequivalents thereof

What is claimed is:
 1. A method for movement based object detection, themethod comprises: receiving or generating a video stream that comprisesa sequence of images; generating image signatures of the images; whereineach image is associated with an image signature that comprisesidentifiers; wherein each identifier identifies a region of interestwithin the image; generating movement information indicative ofmovements of the regions of interest within consecutive images of thesequence of images; searching, based on the movement information, for afirst group of regions of interest that follow a first movement; whereindifferent first regions of interest are associated with different partsof an object; and linking between first identifiers that identify thefirst group of regions of interest; wherein the linking is executedwithout receiving prior knowledge regarding an inclusion of thedifferent parts of the object in the object.
 2. The method according toclaim 1 wherein the linking comprises linking between first imagesignatures that include the first linked identifiers.
 3. The methodaccording to claim 2 wherein the linking comprises adding the firstimage signatures to a first concept structure, the first conceptstructure is associated with the first image.
 4. The method according toclaim 3 comprising: receiving or generating an input image; generating asignature of the input image; comparing the signature of the input imageto signatures of the first concept structure; and determining that theinput image comprises a first object when at least one of the signaturesof the first concept structure matches the signature of the input image.5. The method according to claim 1 comprising: receiving or generatingan input image; generating a signature of the input image; searching inthe signature of the input image for at least one of the firstidentifiers; and determining that the input image comprises the objectwhen the signature of the input image comprises at least one of thefirst identifiers.
 6. The method according to claim 1 comprisinggenerating location information indicative of a location of each regionof interest within each image; wherein the generating movementinformation is based on the location information.
 7. A non-transitorycomputer readable medium for movement based object detection, thenon-transitory computer readable medium comprises: receiving orgenerating a video stream that comprises a sequence of images;generating image signatures of the images; wherein each image isassociated with an image signature that comprises identifiers; whereineach identifier identifies a region of interest within the image;wherein different region of interests include different objects;generating movement information indicative of movements of the regionsof interest within consecutive images of the sequence of images;searching, based on the movement information, for a first group ofregions of interest that follow a first movement; and linking betweenfirst identifiers that identify the first group of regions of interest;wherein the linking is executed without receiving prior knowledgeregarding an inclusion of the different parts of the object in theobject.
 8. The non-transitory computer readable medium according toclaim 7 wherein the linking comprises linking between first imagesignatures that include the first linked identifiers.
 9. Thenon-transitory computer readable medium according to claim 8 wherein thelinking comprises adding the first image signatures to a first conceptstructure, the first concept structure is associated with the firstimage.
 10. The non-transitory computer readable medium according toclaim 9 that stores instructions for: receiving or generating an inputimage; generating a signature of the input image; comparing thesignature of the input image to signatures of the first conceptstructure; and determining that the input image comprises a first objectwhen at least one of the signatures of the first concept structurematches the signature of the input image.
 11. The non-transitorycomputer readable medium according to claim 7 that stores instructionsfor: receiving or generating an input image; generating a signature ofthe input image; searching in the signature of the input image for atleast one of the first identifiers; and determining that the input imagecomprises the object when the signature of the input image comprises atleast one of the first identifiers.
 12. The non-transitory computerreadable medium according to claim 7 that stores instructions forgenerating location information indicative of a location of each regionof interest within each image; wherein the generating movementinformation is based on the location information.
 13. An objectdetector, comprising: an input that is configured to receive a videostream that comprises a sequence of images; a signature generator thatis configured to generate image signatures of the images; wherein eachimage is associated with an image signature that comprises identifiers;wherein each identifier identifies a region of interest within theimage; a movement information unit that is configured to generatemovement information indicative of movements of the regions of interestwithin consecutive images of the sequence of images; an object detectiondetermination unit that is configured to: search, based on the movementinformation, for a first group of regions of interest that follow afirst movement; wherein different first regions of interest areassociated with different parts of an object; and link between firstidentifiers that identify the first group of regions of interest;wherein the linking is executed without receiving prior knowledgeregarding an inclusion of the different parts of the object in theobject.
 14. The object detector according to claim 13 wherein thelinking comprises linking between first image signatures that includethe first linked identifiers.
 15. The object detector according to claim14 wherein the linking comprises adding the first image signatures to afirst concept structure, the first concept structure is associated withthe first image.
 16. The object detector according to claim 15 whereinthe input is configured to receive an input image; wherein the signaturegenerator is configured to generate a signature of the input image; andwherein the object detection determination unit is configured to comparethe signature of the input image to signatures of the first conceptstructure; and to determine that the input image comprises a firstobject when at least one of the signatures of the first conceptstructure matches the signature of the input image.
 17. The objectdetector according to claim 13 wherein the input is configured toreceive an input image; wherein the signature generator is configured togenerate a signature of the input image; and wherein the objectdetection determination unit is configured to search in the signature ofthe input image for at least one of the first identifiers; and todetermine that the input image comprises the object when the signatureof the input image comprises at least one of the first identifiers. 18.The object detector according to claim 13 that is configured to generatelocation information indicative of a location of each region of interestwithin each image; wherein the generating of the movement information isbased on the location information.
 19. The method according to claim 1comprising using the linking between the first identifiers that identifythe first group of regions of interest to verify a previous linkingbetween the first identifiers.
 20. The non-transitory computer readablemedium according to claim 7 that stores instructions for using thelinking between the first identifiers that identify the first group ofregions of interest to verify a previous linking between the firstidentifiers.